

import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_UpperCAmelCase     						=      {
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
  _UpperCAmelCase     						=      """hopper-medium-v2"""
  _UpperCAmelCase     						=      gym.make(env_name)

  _UpperCAmelCase     						=      ValueGuidedRLPipeline.from_pretrained(
      "bglick13/hopper-medium-v2-value-function-hor32",
      env=env,
  )

  env.seed(0)
  _UpperCAmelCase     						=      env.reset()
  _UpperCAmelCase     						=      0
  _UpperCAmelCase     						=      0
  _UpperCAmelCase     						=      1_000
  _UpperCAmelCase     						=      [obs.copy()]
  try:
    for t in tqdm.tqdm(range(T)):
      # call the policy
      _UpperCAmelCase     						=      pipeline(obs, planning_horizon=32)

      # execute action in environment
      _UpperCAmelCase     						=      env.step(denorm_actions)
      _UpperCAmelCase     						=      env.get_normalized_score(total_reward)

      # update return
      total_reward += reward
      total_score += score
      print(
          F'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
          F''' {total_score}'''
      )

      # save observations for rendering
      rollout.append(next_observation.copy())

      _UpperCAmelCase     						=      next_observation
  except KeyboardInterrupt:
    pass

  print(F'''Total reward: {total_reward}''')





lowerCamelCase_    :			Tuple    =	{
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
lowerCamelCase_    :			Union[str, Any]    =	{
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						float:
				UpperCamelCase_:				Any						=    from_type.lower().strip("""s"""    )
				UpperCamelCase_:				int						=    to_type.lower().strip("""s"""    )

				UpperCamelCase_:				Any						=    UNIT_SYMBOL.get(lowerCamelCase  ,     lowerCamelCase    )
				UpperCamelCase_:				str						=    UNIT_SYMBOL.get(lowerCamelCase  ,     lowerCamelCase    )

				if from_sanitized not in METRIC_CONVERSION:
								UpperCamelCase_:				Optional[int]						=    (
								    F'''Invalid \'from_type\' value: {from_type!r}.\n'''
								    F'''Conversion abbreviations are: {", ".join(lowerCamelCase    )}'''
								)
								raise ValueError(lowerCamelCase    )
				if to_sanitized not in METRIC_CONVERSION:
								UpperCamelCase_:				Dict						=    (
								    F'''Invalid \'to_type\' value: {to_type!r}.\n'''
								    F'''Conversion abbreviations are: {", ".join(lowerCamelCase    )}'''
								)
								raise ValueError(lowerCamelCase    )
				UpperCamelCase_:				Union[str, Any]						=    METRIC_CONVERSION[from_sanitized]
				UpperCamelCase_:				str						=    METRIC_CONVERSION[to_sanitized]
				UpperCamelCase_:				Tuple						=    1

				if from_exponent > to_exponent:
								UpperCamelCase_:				Union[str, Any]						=    from_exponent - to_exponent
				else:
								UpperCamelCase_:				Dict						=    -(to_exponent - from_exponent)

				return value * pow(10  ,     lowerCamelCase    )


if __name__ == "__main__":
							from doctest import testmod

							testmod()






